Abstract
Learning from concise educational materials, such as lecture notes and presentation slides, often prompts students to seek additional resources. Newcomers to a subject may struggle to find the best keywords or lack confidence in the credibility of the supplementary materials they discover. To address these problems, we introduce Slide++, an automated tool that identifies keywords from lecture slides, and uses them to search for relevant links, videos, and Q&As. This interactive website integrates the original slides with recommended resources, and further allows instructors to ‘pin’ the most important ones. To evaluate the effectiveness of the tool, we trialled the system in four undergraduate computing courses, and invited students to share their experiences via a survey and focus groups at the end of the term. Students shared that they found the generated links to be credible, relevant, and sufficient, and that they became more confident in their understanding of the courses. We reflect on these insights, our experience of using Slide++, and explore how Large Language Models might mitigate some augmentation challenges.
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Acknowledgements
This research/project is supported by the Ministry of Education, Singapore under its Tertiary Education Research Fund (MOE Reference Number: MOE2021-TRF-013). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Ministry of Education, Singapore. Hady W. Lauw gratefully acknowledges the support by the Lee Kong China Fellowship awarded by Singapore Management University.
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Dinushka, D., Poskitt, C.M., Koh, K.C., Mok, H.N., Lauw, H.W. (2024). Towards Automated Slide Augmentation to Discover Credible and Relevant Links. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2151. Springer, Cham. https://doi.org/10.1007/978-3-031-64312-5_24
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